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The Discrete Element Method (DEM) has emerged as a solution to
predicting load capacities of masonry structures. As one of many
numerical methods and computational solutions being applied to
evaluate masonry structures, further research on DEM tools and
methodologies is essential for further advancement. Computational
Modeling of Masonry Structures Using the Discrete Element Method
explores the latest digital solutions for the analysis and modeling
of brick, stone, concrete, granite, limestone, and glass block
structures. Focusing on critical research on mathematical and
computational methods for masonry analysis, this publication is a
pivotal reference source for scholars, engineers, consultants, and
graduate-level engineering students.
Data Driven Methods for Civil Structural Health Monitoring and
Resilience: Latest Developments and Applications provides a
comprehensive overview of data-driven methods for structural health
monitoring (SHM) and resilience of civil engineering structures,
mostly based on artificial intelligence or other advanced data
science techniques. This allows existing structures to be turned
into smart structures, thereby able to provide intelligible
information about their state of health and performance on a
continuous, relatively real-time basis. Artificial-intelligence
based methodologies are becoming increasingly more attractive for
civil engineering and SHM applications; machine learning and deep
learning methods can be applied and further developed to transform
the available data into valuable information for engineers and
decision makers.
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